1. Field of Invention
Embodiments of the invention described herein pertain to computers. More particularly, but not by way of limitation, one or more embodiments of the invention enable a personalized multi-level reputation based recommendation system and method configured to provide recommendations based on user reputation quantified by historical votes that are extended through network analysis. This enables recommendations based on reputations of recommenders at and beyond first hand level of trust of recommenders, i.e., with or without common votes on items respectively.
2. Description of Related Art
The Internet includes millions of active users that are constantly generating content via blog postings, websites and reviews for example. The potential for providing timely, engaging and valuable information that suits a specific user's specific interest and need is great. Yet, finding desirable information is difficult and information overload is a typical result of searching for valuable information. A user can spend large amounts of time sifting through irrelevant and unimportant or unrelated content and miss potentially valuable content that the user would be interested in.
In the physical world, people often make decisions on issues they have not personally evaluated. Instead, people often rely on recommendations made by people that they trust. The trust is built up over time based on past agreements on related issues. The level of trust that a given individual places in another individual is highly subjective and may be influenced and sometimes specifically informed by the level of trust that third party individuals place on that individual.
Search engines exist that allow for the ranking of web pages, based on the number of links to a web page for example. These search engines do not calculate trust for users, but rather place value on websites based on how highly regarded they are, e.g., as ascertained by the number of links to the site by other highly ranked sites. In some implementations, HTML is simply scanned for hyperlinks to sites and the number of hyperlinks to a given URL and the ranking of the linking site determines the relative perceived importance of the web site. This type of ranking of websites provides search results for web pages that are the most highly linked to. This type of ranking is strictly objective and generates a one-size fits all results to keyword searches. It does not take into account the subjective, individual tastes of each user doing the searching.
Collaborative filtering technologies are used for example by certain websites that sell products. Current collaborative filtering implementations do not provide a wide set of recommenders and recommendations. Generally, current collaborative filtering implementations do not provide deep coverage since users may have voted on (or rated/purchased/used) an item in common with a large set of other users that may have completely different tastes. Specifically, recommendations take into account only those recommenders who have direct votes in common with the user yet who as a whole do not necessarily represent a reputable, expert source of recommendations suited to the tastes of that individual searcher. Hence, these recommendations may be inaccurate since the reputations of the recommenders are not taken into account.
New recommendations are provided based on items that a set of users have rated and are not based on what the best recommenders would suggest for example. Many current implementations are shallow, one level systems that do not follow the real world analogy of finding the best recommendations or recommenders to provide recommendations.
With respect to searching for valued information, there is no known system or method that establishes a multi-level network of users with recorded levels of trust derived from rating activity to provide personalized recommendations based on similar valuations of users. Specifically, there is no known system for example that establishes reputations of recommenders beyond a given user's first hand trust of these recommenders. A system which takes into account the reputations of recommenders beyond the given user's first hand trust would improve the scope and accuracy of the recommendations. Hence, there is a need for a multi-level reputation based recommendation system and method.